Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval=FALSE--------------------------------------------------------------
#
# install.packages("manymodelr")
#
## -----------------------------------------------------------------------------
library(manymodelr)
data("yields", package="manymodelr")
## -----------------------------------------------------------------------------
set.seed(520)
train_set<-createDataPartition(yields$normal,p=0.6,list=FALSE)
valid_set<-yields[-train_set,]
train_set<-yields[train_set,]
ctrl<-trainControl(method="cv",number=5)
m<-multi_model_1(train_set,"normal",".",c("knn","rpart"),
"Accuracy",ctrl,new_data =valid_set)
## -----------------------------------------------------------------------------
m$metric
## -----------------------------------------------------------------------------
head(m$predictions)
## -----------------------------------------------------------------------------
# fit a linear model and get predictions
lin_model <- multi_model_2(mtcars[1:16,],mtcars[17:32,],"mpg","wt","lm")
lin_model[c("predicted", "mpg")]
## -----------------------------------------------------------------------------
multi_lin <- multi_model_2(mtcars[1:16, ], mtcars[17:32,],"mpg", "wt + disp + drat","lm")
multi_lin[,c("predicted", "mpg")]
## -----------------------------------------------------------------------------
lm_model <- fit_model(mtcars,"mpg","wt","lm")
lm_model
## -----------------------------------------------------------------------------
models<-fit_models(df=yields,yname=c("height", "weight"),xname="yield",
modeltype="glm")
## -----------------------------------------------------------------------------
res_residuals <- lapply(models[[1]], add_model_residuals,yields)
res_predictions <- lapply(models[[1]], add_model_predictions, yields, yields)
# Get height predictions for the model height ~ yield
head(res_predictions[[1]])
## -----------------------------------------------------------------------------
fit_models(df=yields,yname=c("height","weight"),
xname=".",modeltype=c("lm","glm"), drop_non_numeric = TRUE)
## -----------------------------------------------------------------------------
extract_model_info(lm_model, "r2")
## -----------------------------------------------------------------------------
extract_model_info(lm_model, "adj_r2")
## -----------------------------------------------------------------------------
extract_model_info(lm_model, "p_value")
## -----------------------------------------------------------------------------
extract_model_info(lm_model,c("p_value","response","call","predictors"))
## -----------------------------------------------------------------------------
# getall correlations
# default pearson
head( corrs <- get_var_corr(mtcars,comparison_var="mpg") )
## -----------------------------------------------------------------------------
# purely demonstrative
get_var_corr(yields,"height",other_vars="weight",
drop_columns=c("factor","character"),method="spearman",
exact=FALSE)
## -----------------------------------------------------------------------------
head(get_var_corr_(yields),6)
## -----------------------------------------------------------------------------
head(get_var_corr_(mtcars,subset_cols=list(c("mpg","vs"),c("disp","wt")),
method="spearman",exact=FALSE))
## -----------------------------------------------------------------------------
plot_corr(mtcars,show_which = "corr",
round_which = "correlation",decimals = 2,x="other_var", y="comparison_var",plot_style = "squares"
,width = 1.1,custom_cols = c("green","blue","red"),colour_by = "correlation")
## -----------------------------------------------------------------------------
# color by p value
# change custom colors by supplying custom_cols
# significance is default
set.seed(233)
plot_corr(mtcars, x="other_var", y="comparison_var",plot_style = "circles",show_which = "signif", colour_by = "p.value", sample(colours(),3))
## -----------------------------------------------------------------------------
head(agg_by_group(yields,.~normal,length))
## -----------------------------------------------------------------------------
head(agg_by_group(mtcars,cyl~hp+vs,sum))
## -----------------------------------------------------------------------------
head(rowdiff(yields,exclude = "factor",direction = "reverse"))
## -----------------------------------------------------------------------------
head(na_replace(airquality, how="value", value="Missing"),8)
## -----------------------------------------------------------------------------
test_df <- data.frame(A=c(NA,1,2,3), B=c(1,5,6,NA),groups=c("A","A","B","B"))
# Replace NAs by group
# replace with the next non NA by group.
na_replace_grouped(df=test_df,group_by_cols = "groups",how="ffill")
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.